Methods and Systems for Identifying Local Search Queries

ABSTRACT

Methods and systems are provided for determining whether a search query with an observed number of occurrences in a set of search queries is a local search query. In accordance with one implementation, a method is provided that comprises determining an expected number of occurrences of a search query and comparing the expected number of occurrences to a threshold. Further, the method includes determining whether the search query is a local search query based, at least in part, on the comparison.

TECHNICAL FIELD

The present disclosure relates to the field of computerized dataprocessing and search query analysis. More particularly, and withoutlimitation, the present disclosure relates to methods and systems fordetermining whether search queries are local search queries that have alocal interest to a geographical region.

BACKGROUND

Search queries, including those that originate from users via acomputerized or electronic submission process, can be analyzed by asearch engine or other processor to identify information correspondingor related to each respective search query. The search queries may besubmitted over an electronic network, such as a collection of networks,spanning numerous localities or geographies, such as the Internet. Usersmay submit search queries from various devices, such as personalcomputers, laptops, PDAs, mobile devices, smart phones, and so on.

Search queries may be determined to have a global or local interest.Search queries that are determined to have a local interest may beuseful in a variety of contexts. Search queries may have a localinterest if, for example, the search queries originate from users in aparticular geographical region at a relatively high frequency. Searchqueries that are determined to originate from a particular geographicalregion at a relatively high frequency can be used to influence searchresults for a user in the particular geographical region or searchresults that are otherwise associated with the geographical region.Search results influenced by local interest can be found to be morerelevant to a search query. Local interest is not limited in type togeographical regions; for example, search queries may also have a localinterest if, for example, the search queries originate from a particulardemographic at a relatively high frequency.

Techniques exist for determining whether a search query has a localinterest. For example, in one technique, a set of search queries andcorresponding IP addresses for the corresponding users' devices may beobtained. The geographical regions associated with the search queriesmay be determined by comparing the corresponding IP addresses to alookup table that stores data linking IP addresses with geographicalregions. If a search query originates from a particular geographicalregion at a relatively high frequency, as compared to other geographicalregions, the search query may be determined to have a local interestwith the particular geographical region. However, if a search query hasa low absolute frequency in a particular geographical region, it mayonly be possible to determine with a low reliability whether the searchquery originates from the particular geographical region at a relativelyhigh frequency, as compared to other geographical regions.

Accordingly, and in view of the foregoing, there is a need for Improvedmethods and systems that are capable of determining whether searchqueries are local search queries that have a local interest to ageographical region. Moreover, there is a need for improved methods andsystems for determining whether a search query has a local interest,including for low absolute frequency search queries. There is also aneed for such methods and systems that overcome the drawbacks andlimitations of conventional search query techniques.

SUMMARY

The present disclosure relates to embodiments for search query analysisand determining whether a set of search queries submitted electronicallyfrom users are local search queries that have a local interest to ageographical region. Moreover, embodiments of the present disclosureinclude methods and systems that are capable of determining whethersearch queries are local search queries or otherwise relevant to ageographical region. Embodiments of the present disclosure also relateto methods and systems that determine whether a search query has a localinterest, including for low absolute frequency search queries. As willbe appreciated, methods and systems consistent with embodiments of thepresent disclosure may be implemented with any combination of hardware,software, and/or firmware, including computerized methods and systemsand those embodied with processors or processing components.

In one embodiment consistent with the present disclosure, a computerizedmethod is provided for determining whether a search query with anobserved number of occurrences in a set of search queries is a localsearch query. The method may include the following steps, wherein one ofmore of the steps are performed by at least one processor: determiningan expected number of occurrences of the search query; comparing theexpected number of occurrences to a threshold; and determining whetherthe search query is a local search query, wherein the local search querydetermination is based, at least in part, on the comparison.

In another embodiment consistent with the present disclosure, acomputerized method is provided for determining whether a search querywith an observed number of occurrences in a set of search queries is alocal search query. The method may include the following steps, whereinone of more of the steps are performed by at least one processor:calculating a first score by comparing the observed number ofoccurrences of the search query in the set of search queries with anexpected number of occurrences of the search query in the set of searchqueries; calculating one or more additional scores by comparing one ormore observed numbers of occurrences of additional search queriesrelated to the search query with one or more expected numbers ofoccurrences of the related search queries; and determining whether thesearch query is a local search query based, at least in part, on thefirst score and the one or more additional scores.

In still another embodiment consistent with the present disclosure, asystem is provided for determining whether a search query with anobserved number of occurrences in a set of search queries is a localsearch query. The system comprises a processor, a memory device, andprogram code stored in the memory. The program code stored in the memorydevice, when executed by the processor, causes the processor to performthe following steps: determining an expected number of occurrences ofthe search query; comparing the expected number of occurrences to athreshold; and determining whether the search query is a local searchquery, wherein the local search query determination is based, at leastin part, on the comparison.

Additional aspects and embodiments consistent with the presentdisclosure will be set forth in part in the description which follows,and in part will be obvious from the description, or may be learned bypractice of the invention, as claimed.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute partof this specification, and together with the description, illustrate andserve to explain the principles of various exemplary embodiments.

FIG. 1 illustrates an exemplary device that may be used for implementingembodiments consistent with the present disclosure.

FIG. 2 illustrates an exemplary system that may be used for implementingembodiments consistent with the present disclosure.

FIG. 3 illustrates an exemplary method for determining whether a searchquery is a local search query.

FIG. 4 illustrates an exemplary method for determining whether a searchquery is a local search query in accordance with a first technique.

FIG. 5 illustrates an exemplary method for determining whether a searchquery is a local search query in accordance with a second technique.

DETAILED DESCRIPTION

Reference will now be made in detail to the exemplary embodiments,examples of which are illustrated in the accompanying drawings. Whereverpossible, the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

Embodiments herein include computer-implemented methods, tangiblenon-transitory computer-readable mediums, and computer-implementedsystems. The computer-implemented methods may be executed, for example,by a processor that receives instructions from a non-transitorycomputer-readable storage medium. Similarly, the systems disclosedherein may include at least one processor and memory, and the memory maybe a non-transitory computer-readable storage medium.

As used herein, a non-transitory computer-readable storage medium refersto any type of physical memory on which information or data readable bya processor may be stored. Examples include random access memory (RAM),read-only memory (ROM), volatile memory, nonvolatile memory, harddrives, CD ROMs, DVDs, flash drives, disks, and any other known physicalstorage medium. Singular terms, such as “memory” and “computer-readablestorage medium,” may additionally refer to multiple structures, such aplurality of memories and/or computer-readable storage mediums.

As referred to herein, a “memory” may comprise any type ofcomputer-readable storage medium unless otherwise specified. Acomputer-readable storage medium may store instructions for execution bya processor, including instructions for causing the processor to performsteps or stages consistent with an embodiment herein. Additionally, oneor more computer-readable storage mediums may be utilized inimplementing a computer-implemented method. The term “computer-readablestorage medium” should be understood to include tangible items andexclude carrier waves and transient signals.

In accordance with certain embodiments, methods and systems are providedfor determining whether a search query is a local search query. In someembodiments, the methods and systems determine an expected number ofoccurrences of the search query. In addition, in some embodiments, themethods and systems compare the expected number of occurrences to athreshold. In some embodiments, the methods and systems determinewhether the search query is a local search query, wherein the localsearch query determination is based, at least in part, on the thresholdcomparison.

In some embodiments, if it is determined that the expected number ofoccurrences is above a threshold, a first set of operations isperformed. For example, a first score may be calculated by comparing theobserved number of occurrences of the search query in the set of searchqueries with the expected number of occurrences of the search query inthe set of search queries. The first score may then be analyzed todetermine whether the search query is a local search query.

In some embodiments, if it is determined that the expected number ofoccurrences is below a threshold, a second set of operations isperformed. For example, a first score may be calculated by comparing theobserved number of occurrences of the search query in the set of searchqueries with the expected number of occurrences of the search query inthe set of search queries. In addition, one or more additional scoresmay be calculated by comparing one or more observed numbers ofoccurrences of search queries related to the search query with one ormore expected numbers of occurrences of the related search queries. Thefirst score and the one or more additional scores may then be analyzedto determine whether the search query is a local search query. In someembodiments, the second set of operations is used to determine whether asearch query is a local search query regardless of the expected numberof occurrences of the search query.

FIG. 1 is a diagram illustrating an exemplary device 100 that may beused for implementing embodiments consistent with the presentdisclosure. Device 100 may be a server, laptop computer, desktopcomputer, PDA, mobile phone, smart phone, or other type of computingdevice. Device 100 may include, among other things, one or more of thefollowing components: central processing unit (CPU) 110 configured toexecute computer program code to perform various processes and methods,including the embodiments herein described; memory device 120, such asRAM, EEPROM, hard disk, and flash memory, to store data and computerprogram code; I/O devices 130, such as keyboards and displays; andnetwork card 140 configured to enable device 100 to send and receivedata.

FIG. 2 is a diagram illustrating an exemplary system 200 that may beused for implementing embodiments consistent with the presentdisclosure. Exemplary system 200 may include a search query database210. Search query database 210 may store data regarding one or moresearch queries, such as, for example, data regarding the text of asearch query and the IP address of a user device or other computingdevice from which a search query originates. Search query database 210may also store other data regarding one or more search queries, such as,for example, the time a search query was made or the demographic of theuser that made the search query. In some embodiments, the search querydatabase 210 includes data regarding search queries from a wide varietyof geographical regions. For example, search query database 210 mayinclude data regarding search queries from a variety of geographicalregions in the United States. In some embodiments, the search querydatabase 210 periodically deletes or archives data that is older than aset amount. For example, the search query database 210 may only retaindata that is no more than one week old.

Search query database 210 may obtain data regarding one or more searchqueries from one or more search query sources 250 via a network 270. Forexample, search query database 210 may obtain data regarding one or moresearch queries from a server (for example, a web server or back-endprocessing server) that processes search queries originating from one ormore user devices.

System 200 may also include a location processor 220. In someembodiments, location processor 220 is configured as a computing devicesuch as, for example, device 100 of FIG. 1. Location processor 220 maydetermine a geographical region that corresponds to an IP addressassociated with a search query by, for example, comparing the IP addressto a lookup table that stores data linking or associating one or more IPaddresses with one or more geographical regions. In some embodiments, ifit is determined that a search query originates from a geographicalregion with a small quantity of other search queries, location processor220 associates the search query with a nearby geographical region havinga larger quantity of other search queries. In addition, in someembodiments, location processor 220 may determine a plurality ofgeographical regions associated with a search query. For example, a cityand state associated with a search query may be determined. Ageographical region may represent both contiguous and non-contiguousgeographical regions.

System 200 may also include a local query processor 230. In someembodiments, local query processor 230 is configured as a computingdevice such as, for example, device 100 of FIG. 1. Local query processor230 may determine whether a search query is a local search query. Thedetermination may be made, for example, using the techniques disclosedbelow with respect to the exemplary embodiments of FIGS. 3-5.

System 200 may also include a report processor 240. In some embodiments,report processor 240 is configured as a computing device such as, forexample, device 100 of FIG. 1. Report processor 240 may generate reportsregarding search queries, such as those determined to be local searchqueries. For example, report processor 240 may generate a reportincluding the text of one or more local search queries and one or moregeographical regions associated with the one or more local searchqueries. By way of example, the reports may provide an indication oftrending data in a geographical region that may be used. One or moreusers 260, which may be, for example, devices associated withadvertisers, customers or internal employees, may receive the generatedreports from system 200 via the network 270.

Network 270 may be any one or more of a variety of networks or othertypes of communication connection know to those skilled in the art.Network 270 may include a network connection, bus, or other type of datalink, such as a hardwire, wireless, or other connection known in theart. For example, network 270 may be the Internet, an intranet network,a local area network, or other wireless or other hardwired connection orconnections by which search query sources 250, system 200, and users 260may communicate and exchange information.

While FIG. 2 depicts location processor 220, local query processor 230,and report processor 240 as three separate devices, in some embodimentssome or all of the functions or operations associated with locationprocessor 220, local query processor 230, and report processor 240 maybe implemented in a single device or distributed with respect to aplurality of devices (e.g., a server farm or a cluster of computers).

FIG. 3 depicts an exemplary method 300 for determining whether a searchquery is a local search query. In some embodiments, exemplary method 300begins by obtaining search data and location data associated with thesearch data (step 310). The search data may be representative of text ofone or more search queries. The location data be representative of oneor more geographical regions associated with the one or more searchqueries.

Method 300 may then cluster the search data into sets of search queriesbased on the location data (step 320). In some embodiments, search datais clustered into sets of search queries corresponding to the samegeographical region. In other embodiments, search data is clustered intosets of search queries corresponding to geographical regions within acertain distance. In some embodiments, the total number of occurrencesof each search query in each set of search queries is also determined.

An expected number of occurrences of a search query in a given set maybe determined (step 330). This determination may be made by firstdetermining the total number of search queries in the given set, thetotal number of search queries in all of the sets of search queries(including the given set), and the number of occurrences of the searchquery in all of the sets of search queries. The total number of searchqueries in the given set may be divided by the total number of searchqueries in all of the sets of search queries, and the result may bemultiplied by the number of occurrences of the search query in all ofthe sets of search queries to determine the expected number ofoccurrences of the search query in the given set.

The determined expected number of occurrences of the search query in thegiven set may then be compared to a threshold value (step 340). In someembodiments, if the expected number is not less than the threshold, afirst technique is applied to determine whether the search query is alocal search query (step 350). In some embodiments, if the expectednumber is less than the threshold, a second technique is applied todetermine whether the search query is a local search query (step 360).However, in alternative embodiments, the first or second technique isapplied without regard to the expected number of occurrences of thesearch query in the given set.

FIG. 4 depicts an exemplary method 400 for determining whether a searchquery is a local search query in accordance with a first technique. Insome embodiments, exemplary method 400 begins by determining a valuecorresponding to the ratio between an observed actual number ofoccurrences of the search query in the given set and the determinedexpected number of occurrences for the search query in the given set(step 410). The ratio value may then be compared to a threshold value(step 420). In some embodiments, if the ratio value is determined to begreater than the threshold value, the search query in the given set isdetermined to be a local search query (step 430). In some embodiments,if the ratio value is determined not to be greater than the thresholdvalue, the search query in the given set is determined not to be a localsearch query (step 440).

For example, the text of a search query may be “metro access” and agiven set may correspond to the geographical region of Washington, D.C.The total number of search queries in all sets may be, for example,130,915,241, the total number of occurrences of “metro access” in allsets may be, for example, 58, the total number of search queries in theset corresponding to Washington, D.C. may be, for example, 1,956,003,and the total number of occurrences of “metro access” in the setcorresponding to Washington, D.C. may be, for example, 49. In thisexample, the expected number of occurrences of “metro access” in the setcorresponding to Washington, D.C. may be equal to((1,956,003/130,915,214)*58), which equals 0.87. Thus, the ratio valuefor “metro access” in the set corresponding to Washington, D.C. may beequal to (49/0.87), which equals 56.32. If the threshold value is setto, for example, 1.5, the ratio value would be determined to be abovethe threshold value, indicating that “metro access” is a local searchquery with respect to the set corresponding to Washington, D.C.

FIG. 5 depicts an exemplary method 500 for determining whether a searchquery is a local search query in accordance with a second technique. Insome embodiments, method 500 begins by determining a value correspondingto the ratio between an observed actual number of occurrences of thesearch query in the given set and the determined expected number ofoccurrences for the search query in the given set (step 510). The ratiovalue may then be compared to a first threshold value (step 520). Insome embodiments, the first threshold is equal to 1.5. In someembodiments, if the ratio value is determined to not be greater than thefirst threshold value, the search query in the given set is determinedto not be a local search query (step 570).

In some embodiments, if the ratio value is determined to be greater thanthe first threshold value, ratio values associated with search queriesrelated to the search query may be determined (step 530). As for thesearch query, a ratio value of a related search query may be determinedby determining the ratio between an observed actual number ofoccurrences of the related search query in the given set and an expectednumber of occurrences for the related search query in the given set.Related search queries may be determined according to one or morealgorithms or may be predetermined. For example, algorithms may beimplemented that determine related search queries on the basis ofassociated or related search terms. By way of example, a search querymay be “fairfax county public library” and may have two related searchqueries, “fopl” (the initials of the search query) and“fairfaxcountypubliclibrary” (the search query with spaces removed).

A determination may be made as to the percentage of ratio valuesassociated with related search queries greater than a second threshold(step 540). In some embodiments, the second threshold is equal to 1.5. Adetermination may then be made as to whether the percentage is greaterthan a third threshold (step 550). In some embodiments, the thirdthreshold is equal to 0.5 (i.e., 50%). In some embodiments, if thepercentage is determined to not be greater than the third thresholdvalue, the search query in the given set is determined to not be a localsearch query (step 570). However, if the percentage is determined to begreater than the third threshold value, the search query in the givenset is determined to be a local search query (step 560).

In some embodiments, one or more clusters of related search queries mayexist, and a search query may be associated with one or more of theclusters of related search queries. Thus, in some embodiments, if asearch query is associated with more than one cluster of related searchqueries, steps 530-570 are repeated for each cluster of related searchqueries. In some embodiments, the search query is determined to be alocal search query if analysis of at least one cluster of related searchqueries indicates that the search query is a local search query. Inother embodiments, the search query is determined to be a local searchquery if analysis of more than some percentage (e.g., 50%) of theclusters of related search queries indicates that the search query is alocal search query.

As an example of the second technique, the text of a search query may be“fairfax county public library” and a given set may correspond to thegeographical region of Washington, D.C. A ratio value, determined in themanner described above, associated with “fairfax county public library”for the set corresponding to Washington, D.C. may be, for example,50.52. As discussed above, search queries related to “fairfax countypublic library” may be determined to be “fcpl” and“fairfaxcountypubliclibrary” (i.e., “fairfax county public library”,“fcpl”, and “fairfaxcountypubliclibrary” may be one cluster of relatedsearch queries). Ratio values, determined in the manner described above,associated with “fcpl” and “fairfaxcountypubliclibrary” for the setcorresponding to Washington, D.C. may be, for example, 50.92 and 0,respectively. Since the ratio value associated with “fairfax countypublic library” is greater than 1.5, and since two out of the threeratio values are greater than 1.5, “fairfax county public library” willbe determined to be a local search query with respect to the setcorresponding to Washington, D.C.

By utilizing related search queries, the second technique discussedabove with reference to FIG. 5 may be used to make a more reliable localsearch query determination for a search query having a relatively lowexpected number of occurrences in a given set. However, in someembodiments, the second technique may also be used for other searchqueries, including search queries having a relatively high expectednumber of occurrences in a given set.

While several embodiments described herein may be implemented todetermine whether a search query is a local search query with referenceto a geographical region, such embodiments may be additionally oralternatively implemented using the disclosed techniques to determinewhether a search query is a local search query with reference to anothertype, such as, for example, a demographic.

Identification of local search queries can provide a number of benefits.For example, a user device in a geographical region may be presentedwith local search queries associated with the geographical region. Thepresented local search queries may allow a user to identify, forexample, trending topics and current events within the geographicalregion. In addition, a database of local search queries may bemaintained that is organized by geographical region. Local searchqueries may also be useful for advertising networks or systems. Forexample, if a user device is determined to originate from a geographicalregion, advertisements generated based on local search queriesassociated with the geographical region may be presented to the userdevice.

While the present disclosure provides examples of one or more processesor apparatuses, it will be appreciated that other processes orapparatuses can be implemented or adopted to be within the scope of theaccompanying claims.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations of theembodiments will be apparent from consideration of the specification andpractice of the disclosed embodiments. For example, the describedimplementations include hardware and software, but systems and methodsconsistent with the present disclosure can be implemented as hardwarealone.

Computer programs based on the written description and methods of thisspecification are within the skill of a software developer. The variousprograms or program modules can be created using a variety ofprogramming techniques. For example, program sections or program modulescan be designed in or by means of Java, C, C++, assembly language, orany such programming languages. One or more of such software sections ormodules can be integrated into a computer system or existingcommunications software.

Moreover, while illustrative embodiments have been described herein, thescope includes any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations based on the presentdisclosure. The elements in the claims are to be interpreted broadlybased on the language employed in the claims and not limited to examplesdescribed in the present specification or during the prosecution of theapplication, which examples are to be construed as non-exclusive.Further, the steps of the disclosed methods can be modified in anymanner, including by reordering steps and/or inserting or deletingsteps. It is intended, therefore, that the specification and examples beconsidered as example only, with a true scope and spirit being indicatedby the following claims and their full scope of equivalents.

What is claimed is:
 1. A method for determining whether a search querywith an observed number of occurrences in a set of search queries is alocal search query, the method comprising the following steps, whereinone or more of the steps are performed by at least one processor:determining an expected number of occurrences of the search query basedon the number of observed occurrences of the search query in a pluralityof sets of search queries; comparing the expected number of occurrencesto a threshold; and determining whether the search query is a localsearch query based, at least in part, on the comparison.
 2. The methodof claim 1, further comprising: determining the expected number ofoccurrences is below the threshold; calculating a first score bycomparing the observed number of occurrences of the search query in theset of search queries with the expected number of occurrences of thesearch query in the set of search queries; calculating one or moreadditional scores by comparing one or more observed numbers ofoccurrences of search queries related to the search query with one ormore expected numbers of occurrences of the related search queries; anddetermining whether the search query is a local search query based, atleast in part, on the first score and the one or more additional scores.3. The method of claim 2, further comprising: determining whether thefirst score is greater than a first threshold; determining a percentageof additional scores that are greater than a second threshold; anddetermining whether the percentage is greater than or equal to a thirdthreshold.
 4. The method of claim 3, wherein the second threshold isequal to the first threshold.
 5. The method of claim 3, wherein thethird threshold is 50 percent.
 6. The method of claim 1, wherein the setof search queries is stored in a database comprising one or moreadditional sets of search queries, and further wherein the expectednumber of occurrences of the search query is determined by multiplyingthe ratio of the number of search queries in the set of search queriesto the number of search queries in the database with the observed numberof occurrences of the search query in the database.
 7. The method ofclaim 1, further comprising: determining the expected number ofoccurrences is above the threshold; calculating a first score bycomparing the observed number of occurrences of the search query in theset of search queries with the expected number of occurrences of thesearch query in the set of search queries; and determining whether thefirst score is greater than a first threshold.
 8. The method of claim 1,wherein the set of search queries is associated with a geographicalregion.
 9. The method of claim 1, further comprising: presenting dataassociated with the determined local search query to a device, the datarepresenting at least one of an advertisement or trending data.
 10. Amethod for determining whether a search query with an observed number ofoccurrences in a set of search queries is a local search query, themethod comprising the following steps, wherein one or more of the stepsare performed by at least one processor: calculating a first score bycomparing the observed number of occurrences of the search query in theset of search queries with an expected number of occurrences of thesearch query in the set of search queries; calculating one or moreadditional scores by comparing one or more observed numbers ofoccurrences of additional search queries related to the search querywith one or more expected numbers of occurrences of the related searchqueries; and determining whether the search query is a local searchquery based, at least in part, on the first score and the one or moreadditional scores.
 11. The method of claim 10, further comprising:determining whether the first score is greater than a first threshold;determining a percentage of additional scores that are greater than asecond threshold; and determining whether the percentage is greater thanor equal to a third threshold.
 12. The method of claim 11, wherein thesecond threshold is equal to the first threshold.
 13. The method ofclaim 11, wherein the third threshold is 50 percent.
 14. The method ofclaim 10, wherein the set of search queries is associated with ageographical region.
 15. The method of claim 10, further comprising:presenting data associated with the determined local search query to adevice, the data representing at least one of an advertisement ortrending data.
 16. A system for determining whether a search query withan observed number of occurrences in a set of search queries is a localsearch query, comprising: a processor; a memory device; program codestored in the memory device, which, when executed by the processor,causes the processor to perform the steps of: determining an expectednumber of occurrences of the search query based on the number ofobserved occurrences of the search query in a plurality of sets ofsearch queries; comparing the expected number of occurrences to athreshold; and determining whether the search query is a local searchquery, wherein the local search query determination is based, at leastin part, on the comparison.
 17. The system of claim 16, wherein theprogram code further causes the processor to perform the steps of:determining the expected number of occurrences is below the threshold;calculating a first score by comparing the observed number ofoccurrences of the search query in the set of search queries with theexpected number of occurrences of the search query in the set of searchqueries; calculating one or more additional scores by comparing one ormore observed numbers of occurrences of search queries related to thesearch query with one or more expected numbers of occurrences of therelated search queries; and determining whether the search query is alocal search query based, at least in part, on the first score and theone or more additional scores.
 18. The system of claim 17, wherein theprogram code further causes the processor to perform the steps of:determining whether the first score is greater than a first threshold;determining a percentage of additional scores that are greater than asecond threshold; and determining whether the percentage is greater thanor equal to a third threshold.
 19. The system of claim 18, wherein thesecond threshold is equal to the first threshold.
 20. The system ofclaim 18, wherein the third threshold is 50 percent.
 21. The system ofclaim 16, wherein the set of search queries is stored in a databasecomprising one or more additional sets of search queries, and furtherwherein the expected number of occurrences of the search query isdetermined by the processor by multiplying the ratio of the number ofsearch queries in the set of search queries to the number of searchqueries in the database with the observed number of occurrences of thesearch query in the database.
 22. The system of claim 16, wherein theprogram code further causes the processor to perform the steps of:determining the expected number of occurrences is above the threshold;calculating a first score by comparing the observed number ofoccurrences of the search query in the set of search queries with theexpected number of occurrences of the search query in the set of searchqueries; and determining whether the first score is greater than a firstthreshold.
 23. The system of claim 16, wherein the set of search queriesis associated with a geographical region.
 24. The system of claim 16,wherein the program code further causes the processor to perform thestep of: presenting data associated with the determined local searchquery to a device, the data representing at least one of anadvertisement or trending data